Industry requires the development of sophisticated autonomous guided vehicles (AGV) with sensory and software capabilities to allow a vision-based awareness of surrounding objects. To achieve this, a closely integrated control system for the AGV together with machine vision capabilities needs to be developed to efficiently and reliably detect objects of interest. Industry application of AGVs require detection of humans and to support that requirement thermal imaging cameras offer a broad set of advantages.
The aim of the study is to develop an AGV that uses a thermal imaging camera to detect a human in its environment. To achieve this, a literature study was done to determine the best type of components that should be used, reveal design issues and what characteristics the system must adhere to. LabVIEW was used to simulate AGV movement and operation together with the control system, develop machine vision capable of background noise filtering and verify the machine vision identification and tracking processes. Based on simulated results, the physical system was built and small modificationsmade to accommodate real world variables. The results indicate that a vision-based approach to detect, track and identify a person on a mobile robot in real time is achievable. It was found that LabVIEW is an excellent tool and platform for building the integrated system and expedites design and implementation. A key implication of this study is to show the versatility of thermal imaging as a method to extract a person from its background independently from current light conditions and in situations where full-colour cameras will fail.